多个中央交易对手市场的系统风险

IF 1.6 3区 经济学 Q3 BUSINESS, FINANCE
Luitgard Anna Maria Veraart, Iñaki Aldasoro
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引用次数: 0

摘要

我们为多个中央对手方(CCP)的清算市场提供了一个风险建模和支付缺口量化框架。基于 CCP 之间共享清算成员这一典型事实,我们建立了一个模型框架,以捕捉 CCP 和清算成员之间的相互关联性。我们使用简单的示例以及基于校准数据的经验证据来说明压力传导机制。此外,我们还展示了压力缓解工具(如一家 CCP 的变动保证金收益扣减)如何对其他 CCP 产生溢出效应。该框架可用于加强中央交易对手委员会的压力测试,目前的压力测试依赖于 "覆盖 2 "标准,该标准要求中央交易对手委员会能够承受其两个最大清算成员的违约。我们的研究表明,如果考虑到通过共享清算成员资格的相互关联性所产生的高阶效应,这两个清算成员是谁会受到很大影响。因此,从金融稳定的角度来看,研究中央交易对手委员会和共享清算成员的整个网络非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systemic risk in markets with multiple central counterparties
We provide a framework for modeling risk and quantifying payment shortfalls in cleared markets with multiple central counterparties (CCPs). Building on the stylized fact that clearing membership is shared among CCPs, we develop a modeling framework that captures the interconnectedness of CCPs and clearing members. We illustrate stress transmission mechanisms using simple examples as well as empirical evidence based on calibrated data. Furthermore, we show how stress mitigation tools such as variation margin gains haircutting by one CCP can have spillover effects on other CCPs. The framework can be used to enhance CCP stress‐testing, which currently relies on the “Cover 2” standard requiring CCPs to be able to withstand the default of their two largest clearing members. We show that who these two clearing members are can be significantly affected if one considers higher‐order effects arising from interconnectedness through shared clearing membership. Looking at the full network of CCPs and shared clearing members is, therefore, important from a financial stability perspective.
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来源期刊
Mathematical Finance
Mathematical Finance 数学-数学跨学科应用
CiteScore
4.10
自引率
6.20%
发文量
27
审稿时长
>12 weeks
期刊介绍: Mathematical Finance seeks to publish original research articles focused on the development and application of novel mathematical and statistical methods for the analysis of financial problems. The journal welcomes contributions on new statistical methods for the analysis of financial problems. Empirical results will be appropriate to the extent that they illustrate a statistical technique, validate a model or provide insight into a financial problem. Papers whose main contribution rests on empirical results derived with standard approaches will not be considered.
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